QSAR study on inhibition of brain 3-hydroxy-anthranilic acid dioxygenase (3-HAO): A molecular connectivity approach

The ability of 4,5-, 4,6-disubstituted and 4,5,6-trisubstituted 3-hydroxyanthranilic acid derivatives to reduce the production of the excitotoxin quinolinic acid (QUIN) by inhibition of brain 3-hydroxyanthranilic acid dioxygenase (3-HAO) has been investigated using molecular connectivity indices (⁰χ^v, ¹χ^v, ²χ^v). The in-vivo inhibition of 3-HAO in rat cortex (pIC₅₀, nM) is used for this purpose. The regression models obtained suggest that the degree of branching of the compounds under study have a dominant role in the observed inhibition potency. The data were used to generate quantitative structure–activity relationship (QSAR) models for estimating the potency of 3-HAO. The information obtained from the correlation should be useful in designing more potent analogues.     

Human protein tyrosine phosphatase 1B inhibitors: QSAR by genetic function approximation

Protein tyrosine phosphatase 1B (PTP 1B), a negative regulator of insulin receptor signaling system, has emerged as a highly validated, attractive target for the treatment of non-insulin dependent diabetes mellitus (NIDDM) and obesity. As a result there is a growing interest in the development of potent and specific inhibitors for this enzyme. This quantitative structure-activity relationship (QSAR) study for a series of formylchromone derivatives as PTP lB inhibitors was performed using genetic function approximation (GFA) technique. The QSAR models were developed using a training set of 29 compounds and the predictive ability of the QSAR model was evaluated against a test set of 7 compounds. The internal and external consistency of the final QSAR model was 0.766 and 0.785. The statistical quality of QSAR models was assessed by statistical parameters r2, r2 (crossvalidated r2), r2pred (predictive r2) and lack of fit (LOF) measure. The results indicate that PTP lB inhibitory activity of the formylchromone derivatives is strongly dependent on electronic, thermodynamic and shape related parameters.     

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic oxazolidinones as antibacterial agents

Oxazolidinones exemplified by eprezolid and linezolid are a new class of antibacterials that are active against Gram positive and anaerobic bacteria including methicillin-resistant Staphylococcus aureus (MRSA), methicillin-resistant Staphylococcus epidermidis (MRSE) and vancomycin resistant enterococci (VRE). In an effort to have a better antibacterial agent in the oxazolidinone class, we have performed three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for a series of tricyclic oxazolidinones. 3D-QSAR studies were performed using the Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) procedures. These studies were performed using 42 compounds; the QSAR model was developed using a training set of 33 compounds. The predictive ability of the QSAR model was assessed using a test set of 9 compounds. The predictive 3D-QSAR models have conventional r(2) values of 0.975 and 0.940 for CoMFA and CoMSIA respectively; similarly, cross-validated coefficient q(2) values of 0.523 and 0.557 for CoMFA and CoMSIA, respectively, were obtained. The CoMFA 3D-QSAR model performed better than the CoMSIA model.     

3D-QSAR studies on natural acetylcholinesterase inhibitors of Sarcococca saligna by comparative molecular field analysis (CoMFA)

We have derived a comprehensive structure–activity relationship (SAR) picture for a new series of natural acetylcholinesterase inhibitors isolated from Sarcococca saligna. A set of 32 previously isolated and tested pregnane-type steroidal alkaloids inhibitors were investigated with respect to their IC₅₀ values (pIC₅₀) against the AChE enzyme in order to derive CoMFA models using atom-based alignment. A highly significant CoMFA model was obtained with r² value of 0.974. The q² (cross validation r²) value also confirms the statistical significance of our model.     

灯盏花挥发性组分结构与保留时间关系研究

通过对有机化合物非氢原子进行分类、参数化染色、建立非氢原子之间的关系得到新的结构描述符.对灯盏花的64种挥发性有机化合物结构进行了参数化表征,运用多元线性回归(MLR)和偏最小二乘回归(PLS)方法构建了化合物结构与色谱保留时间的关系模型.通过“留一法”交互检验对模型的稳定性进行了评价,利用外部样本集对模型的预测能力进...     

Three-dimensional quantitative structure-activity relationship (3 D-QSAR) and docking studies on (benzothiazole-2-yl) acetonitrile derivatives as c-Jun N-terminal kinase-3 (JNK3) inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for 44 (benzothiazole-2-yl) acetonitrile derivatives, inhibiting c-Jun N-terminal kinase-3 (JNK3). It includes molecular field analysis (MFA) and receptor surface analysis (RSA). The QSAR model was developed using 34 compounds and its predictive ability was assessed using a test set of 10 compounds. The predictive 3D-QSAR models have conventional r2 values of 0.849 and 0.766 for MFA and RSA, respectively; while the cross-validated coefficient r(cv)2 values of 0.616 and 0.605 for MFA and RSA, respectively. The results of the QSAR model were further compared with a structure-based analysis using docking studies with crystal structure of JNK3. Ligands bind in the ATP pocket and the hydrogen bond with GLN155 was found to be crucial for selectivity among other kinases. The results of 3D-QSAR and docking studies validate each other and hence, the combination of both methodologies provides a powerful tool directed to the design of novel and selective JNK3 inhibitors.     

3D-QSAR studies on fluroquinolones derivatives as inhibitors for tuberculosis

A quantitative structure activity relationship (QSAR) study was performed on the fluroquinolones known to have anti-tuberculosis activity. The 3D-QSAR models were generated using stepwise variable selection of the four methods - multiple regression (MR), partial least square regression (PLSR), principal component regression (PCR) and artificial neural networks (kNN-MFA). The statistical result showed a significant correlation coefficient q(2) (90%) for MR model and an external test set of (pred_r(2)) -1.7535, though the external predictivity showed to improve using kNN-MFA method with pred_r(2) of -0.4644. Contour maps showed that steric effects dominantly determine the binding affinities. The QSAR models may lead to a better understanding of the structural requirements of anti-tuberculosis compounds and also help in the design of novel molecules.     

Binding affinity prediction of novel estrogen receptor ligands using receptor-based 3-D QSAR methods

We have recently reported the development of a 3-D QSAR model for estrogen receptor ligands showing a significant correlation between calculated molecular interaction fields and experimentally measured binding affinity. The ligand alignment obtained from docking simulations was taken as basis for a comparative field analysis applying the GRID/GOLPE program. Using the interaction field derived with a water probe and applying the smart region definition (SRD) variable selection procedure, a significant and robust model was obtained (q²_LOO=0.921, SDEP=0.345). To further analyze the robustness and the predictivity of the established model several recently developed estrogen receptor ligands were selected as external test set. An excellent agreement between predicted and experimental binding data was obtained indicated by an external SDEP of 0.531. Two other traditionally used prediction techniques were applied in order to check the performance of the receptor-based 3-D QSAR procedure. The interaction energies calculated on the basis of receptor–ligand complexes were correlated with experimentally observed affinities. Also ligand-based 3-D QSAR models were generated using program FlexS. The interaction energy-based model, as well as the ligand-based 3-D QSAR models yielded models with lower predictivity. The comparison with the interaction energy-based model and with the ligand-based 3-D QSAR models, respectively, indicates that the combination of receptor-based and 3-D QSAR methods is able to improve the quality of prediction.     

Comparison in halide binding ability between the unsaturated clusters [Pd3(μ-dppm)3(CO)] and [PdPtCo(μ-dppm)2(CO)3(CNBu)] (dppm = Ph2PCH2PPh2)

The trinuclear clusters [Pd₃(μ-dppm)₃(CO)]²⁺ and [PtPdCo(μ-dppm)₂(CO)₃(CN^tBu)]⁺ exhibit a large and a small cavity, respectively, formed by the phenyl rings of the bridging diphosphine ligands. Their binding constants (K₁₁) with halide ions (X⁻) were obtained by UV-Vis spectroscopy. The binding ability varies as I⁻ > Br⁻ > Cl⁻, and [Pd₃(μ-dppm)₃(CO)]²⁺ > [ptPdCo(μ-dppm)₂-(CO)₃(CN^tBu)]⁺. The MO diagram for the related cluster [Pd₂Co(μ-dppm)₂(CO)₄]⁺ has been addressed theoretically in order to predict the nature of the lowest energy electronic bands. For this class of compounds, the lowest energy bands are assigned to charge transfers from the Co center to the Pd₂ centers.     

基于快鸟影像纹理特性的刺槐林叶面积指数估算

以渭北地区刺槐林为研究对象,通过计算高分辨率影像不同窗口的纹理参数,分别运用一元一次方程、一元二次方程、乘幂模型和指数模型,研究刺槐林野外实测叶面积指数（LAI）与快鸟影像纹理参数间的相关关系,遴选黄土高原地区估算刺槐林LAI最适合的纹理参数以及计算窗口大小.结果表明： 纹理指数对LAI反演精度影响显著,其中角二阶矩阵和熵的估算精度明显高于其他纹理指数;计算窗口的大小显著影响LAI反演精度,非相似度和对比度的反演精度在窗口9×9达到最大,其余纹理指数对于LAI的估算精度随着计算窗口的变大不断降低,在窗口3×3时达到最大;乘幂模型的估算精度低于其他3种方法.     

Molecular docking and 3D-QSAR studies on inhibitors of DNA damage signaling enzyme human PARP-1

Poly (ADP-ribose) polymerase-1 (PARP-1) operates in a DNA damage signaling network. Molecular docking and three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on human PARP-1 inhibitors. Docked conformation obtained for each molecule was used as such for 3D-QSAR analysis. Molecules were divided into a training set and a test set randomly in four different ways, partial least square analysis was performed to obtain QSAR models using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Derived models showed good statistical reliability that is evident from their r2, q2(loo) and r2(pred) values. To obtain a consensus for predictive ability from all the models, average regression coefficient r2(avg) was calculated. CoMFA and CoMSIA models showed a value of 0.930 and 0.936, respectively. Information obtained from the best 3D-QSAR model was applied for optimization of lead molecule and design of novel potential inhibitors.     

Quantitative structure-activity analysis of 5-arylidene-2,4-thiazolidinediones as aldose reductase inhibitors

Design of aldose reductase (ALR2) inhibitors has received considerable attention. Aldose reductase inhibitors, when administered from the onset of hyperglycemia, prevent the progression of polyol accumulation-linked complications. The feasibility that inhibition of aldose reductase provides a pharmacologically direct treatment for diabetic complications that is independent of the control of blood sugar levels has spurred the development of structurally diverse aldose reductase inhibitors. In this work, we report quantitative structure-activity relationship (QSAR) analysis performed by 3D-QSAR analysis, Hansch analysis, and Fujita-Ban analysis on a series of 5-arylidene-2,4-thiazolidinediones as aldose reductase inhibitors. The 2D & 3D-QSAR models were generated using 18 compounds and Fujita-Ban analysis models were obtained using 23 compounds. The predictive ability of the resulting 2D and 3D models was evaluated against a test set of 5 compounds. Analyses of results from the present QSAR study inferred that 3rd position of the phenyl ring and acetic acid substitution at N-position of thiazolidinediones play a key role in the aldose reductase inhibitory activity.     

Computational modeling and design of renin inhibitors

The recently introduced field-based QSAR was employed to develop robust quantitative 3D QSAR models to comprehend the activity of several structurally diverse classes of small molecule renin inhibitors reported in literature. A reasonable predictive model with an r² (pred) of ～0.67 and rmse of 0.79 was achieved for an external validation set of ～150 compounds centered on the model developed using ～450 training set compounds. Based on the developed 3D QSAR models and additional insights gained from reported X-ray structures, opportunity for activity improvements in the [aza]indole scaffold was explored using a carefully designed virtual library of ～2300 compounds. The potential for success of such combined structure-guided and ligand-based approach was justified when the resulting prediction was compared against a representative with supporting experimental results.     

Chemometric modelling of antimalarial activity of aryltriazolylhydroxamates

We have performed quantitative structure–activity relationship (QSAR) and quantitative activity–activity relationship (QAAR) studies for aryltriazolylhydroxamates having antimalarial activity data against both chloroquine-sensitive (D6 clone) and chloroquine-resistant (W2 clone) strains of Plasmodium falciparum to understand the relationships between the biological activity and molecular properties for the design of new compounds. The QSAR studies were performed using 35 compounds among which 26 molecules were taken using k-means clustering technique in the training set for the derivation of the QSAR models and nine molecules were kept as the test-set compounds to evaluate the predictive ability of the derived models. The chemometric tool used for the analysis was the genetic function approximation. The developed models were analysed in terms of their predictive ability, and comparable results were obtained for cross-validated predictive variance (Q ²) and externally predicted variance (R ² _pred) values (0.761 and 0.829, respectively, for the D6 model, 0.708 and 0.748, respectively, for the W2 model and 0.984 and 0.982, respectively for the QAAR model). The QSAR models suggest that the number of methylene groups (between the triazolyl and hydroxamate moieties) and partially negatively charged surface areas of the molecules are important parameters for the antimalarial activity.     

Comparative molecular field analysis and QSAR on substrates binding to cytochrome p450 2D6

In this study, we utilized comparative molecular field analysis (CoMFA) to gain a better understanding of the steric and electrostatic features of the cytochrome P450 2D6 (CYP2D6) active site. The training set consists of 24 substrates with reported K_M values from liver microsomal CYP2D6 spanning an activity range of almost three log units. The low energy conformers were fit by root mean square (RMS) to minaprine at the site of metabolism and to the protonated nitrogen. In this manner, we constructed two CoMFA models, one model with a distance constraint and another without. The model with the distance parameter (non-crossvalidated R²=0.99) was approximately equal to the CoMFA without a distance parameter (non-cross-validated R²=0.98). Validation of our CoMFA was accomplished by predicting the K_M values of 15 diverse CYP2D6 substrates not in the original training set resulting in a predictive R²=0.62. Finally, we also pursued correlations of pK_a and log P with CYP2D6 substrate K_M in an effort to investigate other physicochemical properties.     

Copper(II) promoted imidazolidine ring formation and complexation: A unique reaction course

The reaction of Cu(II) ions with a sodium salt of new Schiff base ligand NaL¹, sodium N-2-methyl pyridine-2-imine benzoate, in alkaline medium produced an imine bond coupled ligand and a novel complex, Na₂[Cu(L³)₂], L³ = 2,5-di(2-benzoic acid)-4-(2-pyridine)-1-(2-methyl-2-pyridine)-imidazolidine. When the reduced form of the sodium salt of the Schiff base ligand, NaL², is employed, a simple hexacoordinated copper(II) complex, [Cu(L²)₂], [L²]⁻ = bis(N-(2-methylpyridine)-2-aminomethylbenzoate), was isolated. The compounds were characterized by spectroscopic methods and the molecular structures of [Cu(L²)₂] and Na₂[Cu(L³)₂] were determined by single-crystal X-ray diffraction methods. Reaction mechanism for the synthesis of, Na₂[Cu(L³)₂], copper(II) promoted imine bond coupling is proposed and discussed. The redox behavior of [Cu(L²)₂] and Na₂[Cu(L³)₂], studied using cyclic voltammetry and electron paramagnetic resonance spectroscopic methods, are also discussed.     

Three-dimensional quantitative structure activity relationships (3-D-QSAR) of antihyperglycemic agents.

A three-dimensional quantitative structure activity relationship study (3-D-QSAR) was performed on a set of thiazolidinedione antihyperglycemic agents using the comparative molecular field analysis (CoMFA) method. The CoMFA models were derived from a training set of 53 compounds. Fifteen compounds, which were not used in model generation were used to validate the CoMFA models. All the compounds were superimposed to the template structure by atom-based and shape-based strategies. The SYBYL QSAR rigid body field fit was also used for aligning the ligands. A total of twelve different alignments were generated. The resulting models exhibited a good cross-validated r2cv values (0.624-0.764) and the conventional r2 values (0.689-0.921). A more robust cross-validation test using cross-validation by 2 groups (leave half out method) was performed 100 times to ascertain the predictiveness of the CoMFA models. The mean of r2cv values from 100 runs ranged from 0.611-0.690. Few models exhibited good external predictivity. These models were then used to define a hypothetical receptor model for antihyperglycemic agents.